114 resultados para Marsh, Doug


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Whilst not true in all cases, the microbial communities that chronically infect the airways of patients with CF can vary little over a year despite antibiotic perturbation. The species present tended to vary more between than within subjects, suggesting that each CF airway infection is unique, with relatively stable and resilient bacterial communities. The inverse relationship between community richness and disease severity is similar to findings reported in other mucosal infections.

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These findings strongly suggest that CFPE do not generally result from increased bacterial density within the airways. Instead, data presented here are consistent with alternative models of pulmonary exacerbation.

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During the winter of 2013/14, much of the UK experienced repeated intense rainfall events and flooding. This had a considerable impact on property and transport infrastructure. A key question is whether the burning of fossil fuels is changing the frequency of extremes, and if so to what extent. We assess the scale of the winter flooding before reviewing a broad range of Earth system drivers affecting UK rainfall. Some drivers can be potentially disregarded for these specific storms whereas others are likely to have increased their risk of occurrence. We discuss the requirements of hydrological models to transform rainfall into river flows and flooding. To determine any general changing flood risk, we argue that accurate modelling needs to capture evolving understanding of UK rainfall interactions with a broad set of factors. This includes changes to multiscale atmospheric, oceanic, solar and sea-ice features, and land-use and demographics. Ensembles of such model simulations may be needed to build probability distributions of extremes for both pre-industrial and contemporary concentration levels of atmospheric greenhouse gases.

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Understanding observed changes to the global water cycle is key to predicting future climate changes and their impacts. While many datasets document crucial variables such as precipitation, ocean salinity, runoff, and humidity, most are uncertain for determining long-term changes. In situ networks provide long time-series over land but are sparse in many regions, particularly the tropics. Satellite and reanalysis datasets provide global coverage, but their long-term stability is lacking. However, comparisons of changes among related variables can give insights into the robustness of observed changes. For example, ocean salinity, interpreted with an understanding of ocean processes, can help cross-validate precipitation. Observational evidence for human influences on the water cycle is emerging, but uncertainties resulting from internal variability and observational errors are too large to determine whether the observed and simulated changes are consistent. Improvements to the in situ and satellite observing networks that monitor the changing water cycle are required, yet continued data coverage is threatened by funding reductions. Uncertainty both in the role of anthropogenic aerosols, and due to large climate variability presently limits confidence in attribution of observed changes.

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One of the prerequisites for achieving skill in decadal climate prediction is to initialize and predict the circulation in the Atlantic Ocean successfully. The RAPID array measures the Atlantic Meridional Overturning Circulation (MOC) at 26°N. Here we develop a method to include these observations in the Met Office Decadal Prediction System (DePreSys). The proposed method uses covariances of overturning transport anomalies at 26°N with ocean temperature and salinity anomalies throughout the ocean to create the density structure necessary to reproduce the observed transport anomaly. Assimilating transport alone in this way effectively reproduces the observed transport anomalies at 26°N and is better than using basin-wide temperature and salinity observations alone. However, when the transport observations are combined with in situ temperature and salinity observations in the analysis, the transport is not currently reproduced so well. The reasons for this are investigated using pseudo-observations in a twin experiment framework. Sensitivity experiments show that the MOC on monthly time-scales, at least in the HadCM3 model, is modulated by a mechanism where non-local density anomalies appear to be more important for transport variability at 26°N than local density gradients.

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Observational analyses of running 5-year ocean heat content trends (Ht) and net downward top of atmosphere radiation (N) are significantly correlated (r~0.6) from 1960 to 1999, but a spike in Ht in the early 2000s is likely spurious since it is inconsistent with estimates of N from both satellite observations and climate model simulations. Variations in N between 1960 and 2000 were dominated by volcanic eruptions, and are well simulated by the ensemble mean of coupled models from the Fifth Coupled Model Intercomparison Project (CMIP5). We find an observation-based reduction in N of -0.31±0.21 Wm-2 between 1999 and 2005 that potentially contributed to the recent warming slowdown, but the relative roles of external forcing and internal variability remain unclear. While present-day anomalies of N in the CMIP5 ensemble mean and observations agree, this may be due to a cancellation of errors in outgoing longwave and absorbed solar radiation.

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Two experiments examined the extent to which erroneous recall blocks veridical recall using, as a vehicle for study, the disruptive impact of distractors that are semantically similar to a list of words presented for free recall. Instructing participants to avoid erroneous recall of to-be-ignored spoken distractors attenuated their recall but this did not influence the disruptive effect of those distractors on veridical recall (Experiment 1). Using an externalised output-editing procedure—whereby participants recalled all items that came to mind and identified those that were erroneous—the usual between-sequence semantic similarity effect on erroneous and veridical recall was replicated but the relationship between the rate of erroneous and veridical recall was weak (Experiment 2). The results suggest that forgetting is not due to veridical recall being blocked by similar events.

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Iron is an essential cofactor for both mycobacterial growth during infection and for a successful protective immune response by the host. The immune response partly depends on the regulation of iron by the host, including the tight control of expression of the iron-storage protein, ferritin. BCG vaccination can protect against disease following Mycobacterium tuberculosis infection, but the mechanisms of protection remain unclear. To further explore these mechanisms, splenocytes from BCG-vaccinated guinea pigs were stimulated ex vivo with purified protein derivative from M. tuberculosis and a significant down-regulation of ferritin light- and heavy-chain was measured by reverse-transcription quantitative-PCR (P ≤0.05 and ≤0.01, respectively). The mechanisms of this down-regulation were shown to involve TNFα and nitric oxide. A more in depth analysis of the mRNA expression profiles, including genes involved in iron metabolism, was performed using a guinea pig specific immunological microarray following ex vivo infection with M. tuberculosis of splenocytes from BCG-vaccinated and naïve guinea pigs. M. tuberculosis infection induced a pro-inflammatory response in splenocytes from both groups, resulting in down-regulation of ferritin (P ≤0.05). In addition, lactoferrin (P ≤0.002), transferrin receptor (P ≤0.05) and solute carrier family 11A1 (P ≤0.05), were only significantly down-regulated after infection of the splenocytes from BCG-vaccinated animals. The results show that expression of iron-metabolism genes is tightly regulated as part of the host response to M. tuberculosis infection and that BCG-vaccination enhances the ability of the host to mount an iron-restriction response which may in turn help to combat invasion by mycobacteria.

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Dynamic electricity pricing can produce efficiency gains in the electricity sector and help achieve energy policy goals such as increasing electric system reliability and supporting renewable energy deployment. Retail electric companies can offer dynamic pricing to residential electricity customers via smart meter-enabled tariffs that proxy the cost to procure electricity on the wholesale market. Current investments in the smart metering necessary to implement dynamic tariffs show policy makers’ resolve for enabling responsive demand and realizing its benefits. However, despite these benefits and the potential bill savings these tariffs can offer, adoption among residential customers remains at low levels. Using a choice experiment approach, this paper seeks to determine whether disclosing the environmental and system benefits of dynamic tariffs to residential customers can increase adoption. Although sampling and design issues preclude wide generalization, we found that our environmentally conscious respondents reduced their required discount to switch to dynamic tariffs around 10% in response to higher awareness of environmental and system benefits. The perception that shifting usage is easy to do also had a significant impact, indicating the potential importance of enabling technology. Perhaps the targeted communication strategy employed by this study is one way to increase adoption and achieve policy goals.

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Background: The validity of ensemble averaging on event-related potential (ERP) data has been questioned, due to its assumption that the ERP is identical across trials. Thus, there is a need for preliminary testing for cluster structure in the data. New method: We propose a complete pipeline for the cluster analysis of ERP data. To increase the signalto-noise (SNR) ratio of the raw single-trials, we used a denoising method based on Empirical Mode Decomposition (EMD). Next, we used a bootstrap-based method to determine the number of clusters, through a measure called the Stability Index (SI). We then used a clustering algorithm based on a Genetic Algorithm (GA)to define initial cluster centroids for subsequent k-means clustering. Finally, we visualised the clustering results through a scheme based on Principal Component Analysis (PCA). Results: After validating the pipeline on simulated data, we tested it on data from two experiments – a P300 speller paradigm on a single subject and a language processing study on 25 subjects. Results revealed evidence for the existence of 6 clusters in one experimental condition from the language processing study. Further, a two-way chi-square test revealed an influence of subject on cluster membership.

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Two methods are developed to estimate net surface energy fluxes based upon satellite-based reconstructions of radiative fluxes at the top of atmosphere and the atmospheric energy tendencies and transports from the ERA-Interim reanalysis. Method 1 applies the mass adjusted energy divergence from ERA-Interim while method 2 estimates energy divergence based upon the net energy difference at the top of atmosphere and the surface from ERA-Interim. To optimise the surface flux and its variability over ocean, the divergences over land are constrained to match the monthly area mean surface net energy flux variability derived from a simple relationship between the surface net energy flux and the surface temperature change. The energy divergences over the oceans are then adjusted to remove an unphysical residual global mean atmospheric energy divergence. The estimated net surface energy fluxes are compared with other data sets from reanalysis and atmospheric model simulations. The spatial correlation coefficients of multi-annual means between the estimations made here and other data sets are all around 0.9. There are good agreements in area mean anomaly variability over the global ocean, but discrepancies in the trend over the eastern Pacific are apparent.

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Previous research has suggested that parents’ aspirations for their children’s academic attainment can have a positive influence on children’s actual academic performance. Possible negative effects of parental over-aspiration, however, have found little attention in the psychological literature. Employing a dual-change score model with longitudinal data from a representative sample of German schoolchildren and their parents (N = 3,530; grades 5 to 10), we showed that parental aspiration and children’s mathematical achievement were linked by positive reciprocal relations over time. Importantly, we also found that parental aspiration that exceeded their expectation (i.e., over-aspiration) had negative reciprocal relations with children’s mathematical achievement. These results were fairly robust after controlling for a variety of demographic and cognitive variables such as children’s gender, age, intelligence, school type, and family SES. The results were also replicated with an independent sample of US parents and their children. These findings suggest that unrealistically high parental aspiration can be detrimental for children’s achievement.